1. Field
The present application relates to medical imaging, and more particularly to medical imaging of percutaneous interventions, such as Transcatheter Aortic Valve Implantation (TAVI) procedures.
2. State of the Art
The trend of minimal invasiveness has increased the importance of imaging in clinical interventions. Due to the small incisions made for the interventions, clinicians can no longer use direct visual inspect to navigate their tools, and instead have to rely on intra-procedural images generated from real-time imaging modalities such as X-ray fluoroscopy, ultrasound echography and intra-procedural magnetic resonance imaging.
Image guided navigation driven by visual inspection of the intra-procedural images inherently suffers from limited accuracy and operator bias due to the subjective evaluation by the operator. This effect is more prominent when navigating in moving areas, such as the thoracic region. Furthermore, the intra-procedural images generated by the real-time imaging modalities typically have reduced image quality, and may not always reveal anatomical structures relevant for the clinical procedure. Contrast liquid can be used to visualize the anatomical structures. However, intra-procedural administration of contrast liquid should be limited to prevent patient harm.
Employing an automatic feature tracking mechanism can provide a solution for these issues. Such a mechanism can provide quantitative information about the position of the interventional tools, and their target location. Using this quantitative information during navigation can eliminate the operator bias, and potentially increase the positioning accuracy. Moreover, quantitative information about the position of image features can be used as a reference point to fuse other images, optionally acquired using different imaging modalities as disclosed, for example, in A. Guéziec et al., “Anatomy-based registration of CT-scan and intraoperative X-ray images for guiding a surgical robot”, IEEE Transactions on Medical Imaging (1998) or U.S. Pat. No. 7,778,688.
A typical tracking process is initialized by indicating the initial position of the feature that is to be tracked. This can either be done automatically or manually by the user. After this, a prediction on the future position of the feature is made. This prediction is based on a dynamical model describing the expected motion of the feature. By measuring the actual position of the feature, the predicted position is then updated together with the model parameters as disclosed, for example, in the paper by M. Isard and A. Blake, “Condensation—conditional density propagation for visual tracking”, International Journal of Computer Vision (1998) or in U.S. Pat. No. 8,223,207. This process is repeated by making a new prediction on the next time step, and is continued until the tracking process is stopped.
When the tracked feature is obscured, the tracking process can be continued by generating new predictions on the feature position for future time steps. However, no measurements can be performed, meaning that no corrections can be made to these predictions. Due to the inevitable differences between the predicted motion and the actual motion, modelling errors will stack, and the predicted feature position will diverge from the true position. Furthermore, the actual motion may be different from the predicted motion due to physiological reaction of the body in response of the insertion of an interventional tool. Consequently, when the feature is obscured, these tracking mechanisms can only provide an accurate position of the tracked object for a limited time.
US Patent Publ. No. 2011/0033094 discloses a method for navigating a therapeutic device to a location by continuing to give an estimated position of the device target location, despite being unable to visually identify this area in intra-procedural X-ray fluoroscopy images. This is achieved by tracking a feature, which is visible during X-ray fluoroscopy, in the vicinity of the device target location assuming the feature experiences the same motion as the device target location. As a result, the geometric relation between the tracked feature and the device target location remains constant under all circumstances. By establishing this geometric relation in a pre-procedural image, the device target location can be derived in the intra-procedural from the position of the tracked feature during X-ray fluoroscopy.
In case of trans-catheter heart valve placement, the aortic annulus is the target location, and is invisible without administration of contrast liquid in intra-procedural X-ray images, while the tracked feature can be any anatomic or artificial object which is distinguishable during X-ray fluoroscopy and has the same motion pattern as the aortic root, typically a calcified area near the valves or a stent or any other deployed device.
Using these tracking mechanisms to guide interventional tools to a certain location can, however, pose problems if the tracked feature resides near the target location of the tool. If the tool approaches its target location, the presence of the tool can, in fact, deform or occlude the tracked feature, preventing an accurate localization by the tracking mechanism.
Tracking such feature during intervention is, in fact, not easy to achieve. As long as the therapeutic device approaches the target location, the annulus (and thus the calcifications) will respond to the presence of the therapeutic device. More particularly, the calcifications tend to deform, which can cause the tracking algorithm to fail. Also, the deformation of the calcifications can modify the geometric relation which is used to reconstruct the target location with a consequent reduction of accuracy, which may be crucial for the success of the intervention. If another feature is used instead of calcification spots, this is subjected to the same problems. Moreover, stent or any other metallic interventional devices tend to move with a different motion relative to motion of the target location. Especially if a stent is selected distal to the coronary ostia, the motion of this stent will be significantly different to the motion of the target location.
There's thus the need to improve the tracking process to avoid positioning errors which may be crucial for the success of the intervention.